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Starts 4 July 2025 16:49
Ends 4 July 2025
Neural Scaling for Small LMs and AI Agents - How Superposition Yields Robust Neural Scaling
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Overview
Join this insightful event to explore the mechanisms behind neural scaling for small language models (LMs) and AI agents, focusing on how superposition contributes to robust and efficient information representation. Discover the reasons behind the enhanced capabilities of larger foundation models, which follow predictable power-law decay patterns, leading to improved performance and efficiency.
This course is offered by YouTube and belongs to the categories of Artificial Intelligence Courses and Computer Science Courses.
Perfect for professionals and enthusiasts keen on understanding the dynamic scaling of neural networks and the pivotal role of superposition in AI development.
Syllabus
- Introduction to Neural Scaling
- Foundations of Superposition in Neural Networks
- Power-Law Patterns in AI Models
- Scaling Behaviors in Small Language Models (LMs)
- Robust Neural Scaling via Superposition
- Practical Implications for AI Agents
- Case Studies and Applications
- Conclusion and Future Perspectives
- Supplementary Materials
Subjects
Computer Science